This dataset contains information on university rankings from around the world. It includes key details such as university name, country, and global rank.

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Context

QS Rankings is the world's most reputed university rankings portfolio. QS ranking publishes QS world university rankings each year and this dataset contains top 100 universities in the world, 2024 according to QS Rankings 2024 edition.

Content

This dataset contains 105 rows and 12 features including,

Inspiration

I was researching universities and found QS ranking report with many useful indicators. I thought data scientists could use this data to find useful insights about various world universities in the world.

Source

I scraped QS Ranking report 2024 for this data.

To know more about the indicators, refer to QS Rankings support.

This dataset is a compilation of processed data on citation and references for research papers including their author, institution and open access info for a selected sample of academics analysed using Microsoft Academic Graph (MAG) data and CORE. The data for this dataset was collected during December 2019 to January 2020.Six countries (Austria, Brazil, Germany, India, Portugal, United Kingdom and United States) were the focus of the six questions which make up this dataset. There is one csv file per country and per question (36 files in total). More details about the creation of this dataset are available on the public ON-MERRIT D3.1 deliverable report.The dataset is a combination of two different data sources, one part is a dataset created on analysing promotion policies across the target countries, while the second part is a set of data points available to understand the publishing behaviour. To facilitate the analysis the dataset is organised in the following seven folders:PRTThe dataset with the file name "PRT_policies.csv" contains the related information as this was extracted from promotion, review and tenure (PRT) policies. Q1: What % of papers coming from a university are Open Access?- Dataset Name format: oa_status_countryname_papers.csv- Dataset Contents: Open Access (OA) status of all papers of all the universities listed in Times Higher Education World University Rankings (THEWUR) for the given country. A paper is marked OA if there is at least an OA link available. OA links are collected using the CORE Discovery API.- Important considerations about this dataset: - Papers with multiple authorship are preserved only once towards each of the distinct institutions their authors may belong to. - The service we used to recognise if a paper is OA, CORE Discovery, does not contain entries for all paperids in MAG. This implies that some of the records in the dataset extracted will not have either a true or false value for the _is_OA_ field. - Only those records marked as true for _is_OA_ field can be said to be OA. Others with false or no value for is_OA field are unknown status (i.e. not necessarily closed access).Q2: How are papers, published by the selected universities, distributed across the three scientific disciplines of our choice?- Dataset Name format: fsid_countryname_papers.csv- Dataset Contents: For the given country, all papers for all the universities listed in THEWUR with the information of fieldofstudy they belong to.- Important considerations about this dataset: * MAG can associate a paper to multiple fieldofstudyid. If a paper belongs to more than one of our fieldofstudyid, separate records were created for the paper with each of those _fieldofstudyid_s.- MAG assigns fieldofstudyid to every paper with a score. We preserve only those records whose score is more than 0.5 for any fieldofstudyid it belongs to.- Papers with multiple authorship are preserved only once towards each of the distinct institutions their authors may belong to. Papers with authorship from multiple universities are counted once towards each of the universities concerned.Q3: What is the gender distribution in authorship of papers published by the universities?- Dataset Name format: author_gender_countryname_papers.csv- Dataset Contents: All papers with their author names for all the universities listed in THEWUR.- Important considerations about this dataset :- When there are multiple collaborators(authors) for the same paper, this dataset makes sure that only the records for collaborators from within selected universities are preserved.- An external script was executed to determine the gender of the authors. The script is available here.Q4: Distribution of staff seniority (= number of years from their first publication until the last publication) in the given university.- Dataset Name format: author_ids_countryname_papers.csv- Dataset Contents: For a given country, all papers for authors with their publication year for all the universities listed in THEWUR.- Important considerations about this work :- When there are multiple collaborators(authors) for the same paper, this dataset makes sure that only the records for collaborators from within selected universities are preserved.- Calculating staff seniority can be achieved in various ways. The most straightforward option is to calculate it as _academic_age = MAX(year) - MIN(year) _for each authorid.Q5: Citation counts (incoming) for OA vs Non-OA papers published by the university.- Dataset Name format: cc_oa_countryname_papers.csv- Dataset Contents: OA status and OA links for all papers of all the universities listed in THEWUR and for each of those papers, count of incoming citations available in MAG.- Important considerations about this dataset :- CORE Discovery was used to establish the OA status of papers.- Papers with multiple authorship are preserved only once towards each of the distinct institutions their authors may belong to.- Only those records marked as true for _is_OA_ field can be said to be OA. Others with false or no value for is_OA field are unknown status (i.e. not necessarily closed access).Q6: Count of OA vs Non-OA references (outgoing) for all papers published by universities.- Dataset Name format: rc_oa_countryname_-papers.csv- Dataset Contents: Counts of all OA and unknown papers referenced by all papers published by all the universities listed in THEWUR.- Important considerations about this dataset :- CORE Discovery was used to establish the OA status of papers being referenced.- Papers with multiple authorship are preserved only once towards each of the distinct institutions their authors may belong to. Papers with authorship from multiple universities are counted once towards each of the universities concerned.Additional files:- _fieldsofstudy_mag_.csv: this file contains a dump of fieldsofstudy table of MAG mapping each of the ids to their actual field of study name.

The QS World University Rankings for 2025 is a list of universities from all over the world, organized to show which ones are the best in various areas. It is widely recognized as one of the most reliable ways to compare higher education institutions. This ranking helps students, researchers, and decision-makers understand how well universities perform in terms of academics, teaching, research, and global connections. Let’s break it down into simple parts so that you can understand it easily.

What’s in the Ranking? The ranking includes several key pieces of information about each university:

University Name: This is simply the name of the school. For example, Harvard University or Oxford University. Ranking Position: This tells you the university’s position on the list, like 1st, 50th, or 200th. A lower number means the university is ranked higher. Country/Region: This shows where the university is located, like the USA, the UK, or Japan. Academic Reputation Score: This score is based on surveys of professors and researchers. They give their opinions on which universities are best for studying and learning. Employer Reputation Score: Employers are asked which universities produce the most skilled graduates. This score shows how good a university is at preparing students for jobs. Faculty-Student Ratio: This measures how many students there are per teacher. A lower number means smaller classes and more personal attention for students. Citations per Faculty: This is about research. It shows how often the university’s studies are mentioned in other research papers. The more citations, the better. International Faculty & Students: This looks at how many teachers and students come from different countries, showing how global and diverse the university is. Why Is This Ranking Useful? There are many ways this ranking can help people:

For Students: It helps students decide where they might want to study. For example, if someone wants a university with a good reputation for teaching and research, they can use this ranking to find the best options. For Universities: Schools can use the rankings to see how they compare to others. If one university is ranked lower than another, it can look at the scores to find ways to improve. For Researchers: Researchers can study the ranking to learn about trends in global education. For example, they might explore why certain regions, like Asia or Europe, have universities that are improving quickly. For Policymakers: Governments and organizations can use the rankings to decide where to invest in education. They can also study which areas of education are most important for the future. What Can We Learn from It? The QS World University Rankings help us learn which universities are leading in academics and research. It also shows us how important global diversity is in education. By understanding these rankings, people can make smarter decisions about studying, teaching, or improving education systems. It’s like a guidebook for the world of universities, helping everyone find the best options and learn from the best practices.

Dataset Name: World University Rankings 2023 - Cleaned

Dataset Source

This dataset is a cleaned and preprocessed version of the "World University Rankings 2023" originally provided by Syed Ali Taqi on Kaggle. The original dataset included 13 features, covering information about universities worldwide.

Dataset Features

1. University Rank
2. Name of University
3. Location
4. No of Student
5. No of Student per Staff
6. International Student
7. Female : Male Ratio
8. Overall Score
9. Teaching Score
10. Research Score
11. Citations Score
12. Industry Income Score
13. International Outlook Score

Description

This cleaned version of the dataset has undergone rigorous preprocessing, including handling missing values and encoding categorical features, resulting in a dataset with enhanced usability and cleanliness. It now consists of 2,341 rows and 2,361 columns, providing valuable insights for data analysis, machine learning, and research in the field of higher education.

Original vs. Cleaned Version Comparison

Original Version

The original version of the "World University Rankings 2023" dataset was a comprehensive collection of data on 1,799 universities across 104 countries and regions. While it provided valuable insights into higher education worldwide, it presented some challenges due to missing values, inconsistencies, and a mix of data types.

Original Dataset Source: World University Rankings 2023

Cleaned Version

In this cleaned version of the dataset, significant efforts have been made to enhance its quality and usability. The following improvements were made:

Handling Missing Values: - All missing values, including NaN and Null values, have been meticulously addressed for every feature in the dataset. - Specifically, missing values in the "Name of University" and "Location" columns have been replaced with meaningful placeholders: "Unknown University" and "Unknown Location," respectively.

Encoding and Transformation: - One-hot encoding has been applied to the "Name of University" and "Location" columns, converting categorical data into a numerical format suitable for analysis and modeling. - The "Female Ratio" and "Male Ratio" columns have been separated, allowing for more straightforward analysis of gender ratios. - "OverAll Score" has been divided into "OverAll Score Min" and "OverAll Score Max" columns, providing insights into the range of scores. - "International Student" values have been encoded as fractional values, making it easier to interpret and analyze. - Several features, including "Female Ratio," "Male Ratio," "OverAll Score Min," "OverAll Score Max," "No of Student," and "International Student," have been encoded as numerical values, improving their compatibility with data analysis and modeling techniques.

These enhancements have transformed the dataset into a cleaned and well-structured resource for data analysis, machine learning, and research in the field of higher education. Researchers and data enthusiasts can now explore and gain valuable insights from this improved dataset with confidence.

Whether you are conducting exploratory data analysis, building predictive models, or conducting research, this cleaned version of the dataset provides a solid foundation for your analytical endeavors.

GitHub Repository

For more details on the data preprocessing steps and to access the cleaned dataset, you can visit the GitHub repository where the preprocessing was performed: GitHub Repository

If you find value in this "World University Rankings 2023 - Cleaned" dataset, please consider upvoting it on Kaggle to boost its visibility. Additionally, star our GitHub repository to show your support for the data preprocessing efforts. Your support is greatly appreciated!

Higher Education Institutions in the United States of America Dataset

This repository contains a dataset of higher education institutions in the United States of America. This dataset was compiled in response to a cybersecurity research of American higher education institutions' websites [1]. The data is being made publicly available to promote open science principles [2].

Data

The data includes the following fields for each institution:

• Id: A unique identifier assigned to each institution.
• Region: The federal state in which the institution is located.
• Name: The full name of the institution.
• Category: Indicates whether the institution is public or private.
• Url: The website of the institution.

Methodology

The dataset was obtained from the Higher Education Integrated Data System (IPEDS) website [3], which is administered by the National Center for Education Statistics (NCES). NCES serves as the primary federal entity for collecting and analyzing education-related data in the United States. The data was collected on February 2, 2023.

The initial list of institutions was derived from the IPEDS database using the following criteria: (1) US institutions only, (2) degree-granting institutions, primarily bachelor's or higher, and (3) industry classification, which includes: public 4 - year or above, private not-for-profit 4 years or more, private for-profit 4 years or more, public 2 years, private not-for-profit 2 years, private for-profit 2 years, public less than 2 years, private not-for-profit for-profit less than 2 years and private for-profit less than 2 years.

The following variables have been added to the list of institutions: Control of the institution, state abbreviation, degree-granting status, Status of the institution, and Institution's internet website address. This resulted in a report with 1,979 institutions.

The institution's status was labeled with the following values: A (Active), N (New), R (Restored), M (Closed in the current year), C (Combined with another institution), D (Deleted out of business), I (Inactive due to hurricane-related issues), O (Outside IPEDS scope), P (Potential new/add institution), Q (Potential institution reestablishment), W (Potential addition outside IPEDS scope), X ( Potential restoration outside the scope of IPEDS) and G (Perfect Children's Campus).

A filter was applied to the report to retain only institutions with an A, N, or R status, resulting in 1,978 institutions. Finally, a data cleaning process was applied, which involved removing the whitespace at the beginning and end of cell content and duplicate whitespace. The final data were compiled into the dataset included in this repository.

Usage

This data is available under the Creative Commons Zero (CC0) license and can be used for any purpose, including academic research purposes. We encourage the sharing of knowledge and the advancement of research in this field by adhering to open science principles [2].

If you use this data in your research, please cite the source and include a link to this repository. To properly attribute this data, please use the following DOI: 10.5281/zenodo.7614862

Contribution

If you have any updates or corrections to the data, please feel free to open a pull request or contact us directly. Let's work together to keep this data accurate and up-to-date.

Acknowledgment

We would like to acknowledge the support of the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within the project "Cybers SeC IP" (NORTE-01-0145-FEDER-000044). This study was also developed as part of the Master in Cybersecurity Program at the Instituto Politécnico de Viana do Castelo, Portugal.

References

1. Pending.
2. S. Bezjak, A. Clyburne-Sherin, P. Conzett, P. Fernandes, E. Görögh, K. Helbig, B. Kramer, I. Labastida, K. Niemeyer, F. Psomopoulos, T. Ross-Hellauer, R. Schneider, J. Tennant, E. Verbakel, H. Brinken, and L. Heller, Open Science Training Handbook. Zenodo, Apr. 2018. [Online]. Available: [https://doi.org/10.5281/zenodo.1212496]
3. Integrated Postsecondary Education Data System, "Compare Institutions", Fev 2023. [online]. Available: https://nces.ed.gov/ipeds/use-the-data

Data research project by the Centre for Higher Education Transformation (CHET) and the Higher Education Research and Advocacy Network in Africa (HERANA) into eight African 'flagship' universities. The dataset consists primarily of data on student enrolments, graduate outputs, academic staff and knowledge outputs at the eight universities. The findings of the study are published in the report "An Empirical Overview of Eight Flagship Universities in Africa (2001 - 2011). The report describes the collecting and analysis of cross-national higher education data for the group of eight universities in Africa. It concludes with an analysis of performance which focuses on the links between high-level academic staffing resources and high-level knowledge outputs. These eight universities are described as flagship universities because each is the most prominent public university in its country, and because all of the universities have broad flagship goals built into their vision and mission statements.

Colleges and Universities This feature layer, utilizing data from the National Center for Education Statistics (NCES), displays colleges and universities in the U.S. and its territories. NCES uses the Integrated Postsecondary Education Data System (IPEDS) as the "primary source for information on U.S. colleges, universities, and technical and vocational institutions." According to NCES, this layer "contains directory information for every institution in the 2023-24 IPEDS universe. Includes name, address, city, state, zip code and various URL links to the institution"s home page, admissions, financial aid offices and the net price calculator. Identifies institutions as currently active, and institutions that participate in Title IV federal financial aid programs for which IPEDS is mandatory." University of the District of ColumbiaData currency: 2023Data source: IPEDS Complete Data FilesData modification: Removed fields with coded values and replaced with descriptionsFor more information: Integrated Postsecondary Education Data SystemSupport documentation: Data DictionaryFor feedback, please contact: ArcGIScomNationalMaps@esri.com U.S. Department of Education (ED) Per ED, The mission of the Department of Education (ED) is to promote student achievement and preparation for global competitiveness by fostering educational excellence and ensuring equal access for students of all ages.

This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2017-2018 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 2675 new records, modifications to the spatial location and/or attribution of 19836 records, the removal of 254 records no longer applicable. Additionally, 10,870 records were removed that previously had a STATUS value of 2 (Unknown; not represented in the most recent PSS data) and duplicate records identified by ORNL.

Response variable: overall_score

Scenario It is a difficult task to find an overall measure of quality for higher-education institutions, as there are many areas of work most universities worldwide involve themselves in, such as teaching, research, and knowledge exchange. Nevertheless, to decide on a way to predict the overall quality of an institution would be desirable for those who want to make an informed decision of whether to engage with a specific university, as a student, research collaborator or as industry partner.

This dataset contains records of many universities, offering data on performance metrics, student data and descriptive data. The goal is to forecast the value for the variable overall_score, by employing models suitable for regression problems. Find out what characteristics have the most influence on the general quality of institutions of higher learning.

Columns Description name: Name of the university

scores_teaching: The teaching quality of the university, scored out of 100

scores_research: The research quality of the university, scored out of 100

scores_citations: Citation volume and of academics based at the university, scored out of 100

scores_international_outlok: The university’s level of engagement with international partners, scored out of 100 record_type: Category of the record

member_level: Level of membership

location: Country where the university is located

stats_number_students: The number of students enrolled at the university

stats_student_staff_ratio: Number of staff members per student stats_pc_intl_students: The percentage of enrolled students that are classed as international

stats_female_male_ratio: The ration of female students versus male students

subjects_offered: The range of subject areas that are taught at the university

closed: Whether the university is currently closed to new applicants

unaccredited: Whether the university is currently unaccredited

overall_score: The overall quality of the university, scored out of 100

This dataset offers a set of statistics on the number of students enrolled from 2006-07 to 2022-23 per public institution under the supervision of the French Ministry of Higher Education: universities, Technology Universities, Large Institutions, COMUE, Normal Graduate Schools, Central Schools, INSA, Other Engineering Schools... Unless otherwise noted, the indicators proposed in this dataset do not take into account double CPGE registrations The number of students enrolled in parallel in IFSI (Institutes for Nursing Training) is not taken into account in the number of institutions. **** The data are taken from the Student Monitoring Information System (SISE). Registrations are observed on January 15, except for the University of New Caledonia, which has additional time to take into account the Southern calendar. Each line of this dataset provides an institution’s statistics for one academic year. This game unitely declines a set of variables on the student (sex, baccalaureate, age at the baccalaureate, national attractiveness, international attractiveness) and the training he mainly follows (cursus LMD, type of diploma, diploma, major discipline, discipline and disciplinary sector). The geographical data provided in this game relate to the seat of the institution and not the actual location of the training followed by the student. Cross-sectional and more detailed data are available in the dataset “Staff of students enrolled in public institutions under the supervision of the Ministry of Higher Education](https://data.enseignementsup-recherche.gouv.fr/explore/dataset/fr-esr-sise-effectifs-d-etudiants-inscrits-esr-public/)”. National Framework of Training and Conventions EPSCP-CPGE: impacts on measured workforce changes Two regulatory provisions impact developments from 2018-19 onwards and create statistical breaks: - The new National Training Framework (CNF), put in place for Bachelor’s degrees. The CNF significantly reduces the number of diploma titles. Some of these titles have become more precise, leading to an easier ranking by discipline: this is the case for science licences, less frequently classified in “Plurisciences”, but more in “fundamental sciences and applications” or “sciences of nature and life”. On the other hand, other titles are more general, particularly in literary disciplines (e.g. license mention Humanities) and are more frequently classified as “plurilettres, languages, humanities”. - The progressive implementation of agreements between high schools with preparatory classes for the Grandes écoles (CPGE) and the public institutions of a scientific, cultural and professional nature (EPSCP), of which universities belong, significantly increases the number of LMD license registrations from this year onwards, even if double enrolments were already possible and effective before. University enrolments include these double registrations. These two developments mainly impact the workforce detailed by discipline in L1, which hosts the vast majority of new entrants. The impact on total staff is more marginal. Developments taking into account double listings are at constant regulatory scope. — In 2015-2016 the 2014-15 data for these institutions were renewed: University of New Caledonia, ENS Cachan, ENS Rennes. For more information on this dataset, see dataset documentation.

This layer serves as the authoritative geographic data source for California's K-12 public school locations during the 2022-23 academic year. Schools are mapped as point locations and assigned coordinates based on the physical address of the school facility. The school records are enriched with additional demographic and performance variables from the California Department of Education's data collections. These data elements can be visualized and examined geographically to uncover patterns, solve problems and inform education policy decisions.The schools in this file represent a subset of all records contained in the CDE's public school directory database. This subset is restricted to K-12 public schools that were open in October 2022 to coincide with the official 2022-23 student enrollment counts collected on Fall Census Day in 2022 (first Wednesday in October). This layer also excludes nonpublic nonsectarian schools and district office schools.The CDE's California School Directory provides school location other basic school characteristics found in the layer's attribute table. The school enrollment, demographic and program data are collected by the CDE through the California Longitudinal Achievement System (CALPADS) and can be accessed as publicly downloadable files from the Data & Statistics web page on the CDE website. Schools are assigned X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy. Most schools are mapped to the school structure or centroid of the school property parcel and are individually verified using aerial imagery or assessor's parcels databases. Schools are assigned various geographic area values based on their mapped locations including state and federal legislative district identifiers and National Center for Education Statistics (NCES) locale codes.

This dataset presents the number of teaching staff in public colleges and universities, categorized by country, university title, and gender. It supports higher education workforce analysis and planning.

This is the second research project in which the Centre for Higher Education Transformation’s Higher Education Research and Advocacy Network in Africa (HERANA) project collected data on eight African 'flagship' universities. The findings of the study are published in the report An Empirical Overview of Eight Flagship Universities in Africa 2001–2011. The report describes the collecting and analysis of cross-national higher education data for the group of eight ‘flagship’ universities in Africa. It concludes with an analysis of performance which focuses on the links between high-level academic staffing resources and high-level knowledge outputs. These eight universities are described as flagship universities because each is the most prominent public university in its country, and because all of the universities have broad flagship goals built into their vision and mission statements. The dataset from this study consists primarily of data on student enrolments, graduate outputs, academic staff and knowledge outputs at th e eight universities.

This layer serves as the authoritative geographic data source for California's K-12 public school locations during the 2024-25 academic year. Schools are mapped as point locations and assigned coordinates based on the physical address of the school facility. The school records are enriched with additional demographic and performance variables from the California Department of Education's data collections. These data elements can be visualized and examined geographically to uncover patterns, solve problems and inform education policy decisions.The schools in this file represent a subset of all records contained in the CDE's public school directory database. This subset is restricted to TK-12 public schools that were open in October 2024 to coincide with the official 2024-25 student enrollment counts collected on Fall Census Day in 2024 (first Wednesday in October). This layer also excludes nonpublic nonsectarian schools and district office schools.The CDE's California School Directory provides school location other basic school characteristics found in the layer's attribute table. The school enrollment, demographic and program data are collected by the CDE through the California Longitudinal Achievement System (CALPADS) and can be accessed as publicly downloadable files from the Data & Statistics web page on the CDE website. Schools are assigned X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy. Most schools are mapped to the school structure or centroid of the school property parcel and are individually verified using aerial imagery or assessor's parcels databases. Schools are assigned various geographic area values based on their mapped locations including state and federal legislative district identifiers and National Center for Education Statistics (NCES) locale codes.

The Supplemental Colleges feature class/shapefile contains additional postsecondary education features that are not included in the National Center for Education (NCES) Integrated Post Secondary Education System (IPEDS). Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Any results or conclusions based on the POPULATION, TOT_ENROLL, and TOT_EMP values contained in this layer should be verified. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete feature class information is available in the “Entities and Attributes” metadata section for these and other field details. Geographical coverage is detailed in the Place Keyword section of the metadata and the thumbnail graphic above. This feature class does not have a relationship class but is related to Colleges and Universities by the IPEDS ID. This release includes updates to the TYPE and STATUS fields based on the current release of IPEDS on 2460 records. One (1) new record was added and 15 were removed as their parent records are no longer in the IPEDS data or IPEDS now accounts for that institution. New UNIQUEID values have been assigned to each record using the new ID convention of parent campus IPEDSID with a 2 digit sequential number concatenated to the end. NCESNAME, BASENAME, and STFIPS fields have been removed as they contain redundant information present in other fields. The ENROLL field has been renamed to TOT_ENROLL to be more consistent with the Colleges and Universities datasets. The ENROLLCALC field has been removed as ORNL no longer performs any enrollment calculations. Records where ENROLLCALC was equal to 1, the TOT_ENROLL field was set to -999. This release includes 373 new records and removal of 59 records.

This layer was taken from a national data set of school locations. It includes public and private schools. . The private and public schools were designated private by researching their website or sites related to finding private schools. . The national dataset was clipped to only include schools within the boundary of the Regional Resources Inventory. Any schools designated as "historic" have been removed from this dataset to make it more efficient for modern day analysis. U.S. Geographic Names Information System Schools represents the Federal standard for geographic nomenclature and contains information about the proper names and locations of physical and cultural geographic features located throughout the United States and its Territories. The U.S. Geological Survey developed the Geographic Names Information System (GNIS) for the U.S. Board on Geographic Names, a Federal inter-agency body chartered by public law to maintain uniform feature name usage throughout the Government and to promulgate standard names to the public.

We provide data describing the 78 largest Italian Universities from several perspectives, including scientific research, administrative and economic point of view. In particular, associated with each University, we have the following data. (a) The list of the 30 most representative research keywords, automatically extracted from titles, abstracts and other possible metadata of all the research publications available for that University in Scopus database at October 2022. (b) The Extended_name of the University, Status, University_Type, State_status, number of Managerial and Administrative Staff, Teaching Staff and Researchers, Phd Diplomas, Phd Enrollments, Enrolled Undergraduates, Enrolled Graduates, Graduates, Master I Lv Graduates, Enrolled Master's Degree I Lv, Master II Lv, Graduates Enrolled, Master II Lv, Graduates Specialistic Schools and Enrolled Specialistic Schools were extracted from USTAT database for the years 2016-2018. (c) The data of educational income, Income from Commissioned Research and Technology Transfer, Income from Research with competitive funding, Own Income, Contributions from others (private), Contributions from others (public), Contributions from universities, Contributions from the European Union and the Rest of the World, Contributions from other local governments, Contributions Regions and Autonomous Provinces, MIUR and other central government grants, Operating Costs, Current Management Costs, Managerial and administrative personnel costs, Research and teaching staff cost, Cost of Lecturers and Researchers, Cost Scientific Collaborators, Cost of Contract Teachers, Cost of Language Experts, Other research and teaching personnel costs, Personnel Costs, Scientific equipment, Concessions, licenses and trademarks, Patent rights are extracted from the unique University Balance Sheet of each university for the years 2016-2018. These data were of difficult availability; they have been extracted from several heterogeneous sources and have been automatically checked, cleaned from errors, integrated, missing values have been imputed as much as possible. However, due to large missing portions in the sources, they still contain several missing parts. Nonetheless, they represent a powerful snapshot of the Italian Universities, and can be of interest to researchers for many analyses of the Italian academic world. All the sources of the openly available data are provided.